scholarly journals Method of creation of a two-level neural network structure for solving problems in mechanical engineering

2021 ◽  
Vol 2131 (3) ◽  
pp. 032003
Author(s):  
A Volkov ◽  
O Varlamov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information System ◽  
Big Data ◽  
Network Structure ◽  
Mechanical Engineering ◽  
Level Structure ◽  
Network Analyzers ◽  
Neural Network Structure ◽  
Intelligent Information

Abstract There is an industrial trend of increasing technological effectiveness of production and growing autonomy of factories, resulting in generation of huge amounts of raw data that can be used to improve efficiency and continuity of industrial plants operation. Big data collection, processing and analysis technologies are already being actively implemented. Further use of big data will help with a variety of tasks, such as optimization and process monitoring, quality control of equipment and produced parts, modeling and forecasting of the facility operation and other mechanical engineering challenges. A new method of creation of a two-level neural network structure is proposed, designed to solve a number of problems by training individual neural networks for each subset of data used in the task at hand. This method combines two levels of information processing: the first level of the neural network classifier and the second level, which includes several neural network analyzers. Depending on the specific subject area and the data sets available, it is possible to use the method to solve various problems in mechanical engineering. The method allows to add new neural network analyzers and expand the scope of application. The practical application of the method in solving the problem of text message sentiment analysis is shown and an example of the Python programming language software implementation of the two-level structure is given. Use cases for the two-level structure method in mechanical engineering tasks are proposed. In addition, the proposed method can be used as a part of the hybrid intelligent information system that includes mivar expert systems. Combining neural networks with mivar expert systems as part of a hybrid intelligent information system is a promising direction for the development of artificial intelligence for mechanical engineering.

scholarly journals Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lingfeng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Rating Data ◽  
Tv Program ◽  
Neural Network Structure ◽  
Rating Prediction ◽  
Tv Show

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

Copyright Protection for Software 2.0?

2021 ◽  
pp. 323-340
Author(s):  
Hao-Yun Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Structure ◽  
Copyright Protection ◽  
Computer Programs ◽  
Copyright Law ◽  
Data Sets ◽  
Copyright Laws ◽  
Neural Network Structure ◽  
Over Time

Traditionally, software programmers write a series of hard-coded rules to instruct a machine, step by step. However, with the ubiquity of neural networks, instead of giving specific instructions, programmers can write a skeleton of code to build a neural network structure, and then feed the machine with data sets, in order to have the machine write code by itself. Software containing the code written in this manner changes and evolves over time as new data sets are input and processed. This characteristic distinguishes it markedly from traditional software, and is partly the reason why it is referred to as ‘software 2.0’. Yet the vagueness of the scope of such software might make it ineligible for protection by copyright law. To properly understand and address this issue, this chapter will first review the current scope of computer program protection under copyright laws, and point out the potential inherent issues arising from the application of copyright law to software 2.0. After identifying related copyright law issues, this chapter will then examine the possible justification for protecting computer programs in the context of software 2.0, aiming to explore whether new exclusivity should be granted or not under copyright law, and if not, what alternatives are available to provide protection for the investment in the creation and maintenance of software 2.0.

Neural Network Structure Analysis Based on Hierarchical Force-Directed Graph Drawing for Multi-Task Learning

2015 ◽  
Vol 19 (2) ◽  
pp. 225-231 ◽  
Author(s):  
Atsushi Shibata ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Directed Graph ◽  
Network Structure ◽  
Graph Drawing ◽  
Training Data ◽  
Specific Task ◽  
Motion System ◽  
Close Relationship ◽  
Neural Network Structure

A hierarchical force-directed graph drawing is proposed for the analysis of a neural network structure that expresses the relationship between multitask and processes in neural networks represented as neuron clusters. The process revealed by our proposal indicates the neurons that are related to each task and the number of neurons or learning epochs that are sufficient. Our proposal is evaluated by visualizing neural networks learned on the Mixed National Institute of Standards and Technology (MNIST) database of handwritten digits, and the results show that inactive neurons, namely those that do not have a close relationship with any tasks, are located on the periphery part of the visualized network, and that cutting half of the training data on one specific task (out of ten) causes a 15% increase in the variance of neurons in clusters that react to the specific task compared to the reaction to all tasks. The proposal aims to be developed in order to support the design process of neural networks that consider multitasking of different categories, for example, one neural network for both the vision and motion system of a robot.

Genetic Programming for Letters Identification Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 642-645
Author(s):  
Yan Hui Liu ◽  
Zhi Peng Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Programming ◽  
Network Structure ◽  
High Accuracy ◽  
Identification System ◽  
Neural Network Structure ◽  
Hidden Layer

According to the problem that the letters identification is not high accuracy using neural networks, in this paper, an optimal neural network structure is designed based on genetic algorithm to optimize the number of hidden layer. The English letters can be identified by optimal neural network. The results obtained in the genetic programming optimizations are very satisfactory. Experiments show that the identification system has higher accuracy and achieved good ideal letters identification effect.

Application of Artificial Neural Networks for Diagnosing Acute Appendicitis

2013 ◽  
Vol 479-480 ◽  
pp. 445-450
Author(s):  
Sung Yun Park ◽  
Sangjoon Lee ◽  
Jae Hoon Jeong ◽  
Sung Min Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Acute Appendicitis ◽  
Network Structure ◽  
Scoring System ◽  
Probabilistic Neural Network ◽  
Clinical Scoring ◽  
Neural Network Structure ◽  
Artificial Neural

The purpose of this study is to develop an appendicitis diagnosis system, by using artificial neural networks (ANNs). Acute appendicitis is one of the most common surgical emergencies of the abdomen. Various methods have been developed to diagnose appendicitis, but these methods have not shown good performance in the Middle East and Asia, or even in the West. We used the structures of ANNs with 801 patients. These various structures are a multilayer neural network structure (MLNN), a radial basis function neural network structure (RBF), and a probabilistic neural network structure (PNN). The Alvarado clinical scoring system was used for comparison with the ANNs. The accuracy of MLNN, RBF, PNN, and Alvarado was 97.84%, 99.80%, 99.41% and 72.19%, respectively. The AUC of MLNN, RBF, PNN, and Alvarado was 0.985, 0.998, 0.993, and 0.633, respectively. The performance of ANNs was significantly better than the Alvarado clinical scoring system (P<0.001). The models developed to diagnose appendicitis using ANNs showed good performance. We consider that the developed models can help junior clinical surgeons diagnose appendicitis.

COMPARATIVE ANALYSIS OF VARIOUS METHODS OF SURFACE ROUGHNESS IN DRILLING

2006 ◽  
Vol 05 (01) ◽  
pp. 75-87 ◽  
Author(s):  
C. SANJAY
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Roughness ◽  
Comparative Analysis ◽  
Statistical Analysis ◽  
Network Structure ◽  
Mathematical Analysis ◽  
Cutting Speed ◽  
Machining Time ◽  
Neural Network Structure

Drilling is one of the most common and fundamental machining processes. Since approximately 40% of all the cutting operations are drilling in industry. It is most frequently performed, material removing process and is used as a preliminary step for many operations, such as reaming, tapping and boring. Because of their importance in nearly all production operations twist drills have been the subject of numerous investigations. Surface finish quality of a machined work piece is an issue of main concern to the manufacturing industry. The aim of the present work is to identify suitable parameters for the prediction of surface roughness. Back propagation neural networks were used for detection of surface roughness. Drill diameter, cutting speed, feed, and machining time were given as inputs to the neural network structure and surface roughness was estimated. Drilling experiments with 10 mm drill size were performed at three cutting speeds and feeds. The number of neurons were selected from 1,2,3,…, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The best structure of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. For statistical analysis, it was assumed that surface roughness depends on cutting speed, feed and machining time. For the mathematical analysis inverse coefficient matrix method was used for calculating the estimated values of surface roughness. Comparative analysis has been done between the actual values and the estimated values obtained by statistical analysis, mathematical analysis and neural network structure.

scholarly journals LTR-MDTS structure - a structure for multiple dependent time series prediction

2017 ◽  
Vol 14 (2) ◽  
pp. 467-490 ◽  
Author(s):  
Predrag Pecev ◽  
Milos Rackovic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Network Structure ◽  
Time Series Prediction ◽  
Back Propagation ◽  
Time Lapse ◽  
Multilayered Perceptron ◽  
Neural Network Structure ◽  
Mlp Neural Networks

The subject of research presented in this paper is to model a neural network structure and appropriate training algorithm that is most suited for multiple dependent time series prediction / deduction. The basic idea is to take advantage of neural networks in solving the problem of prediction of synchronized basketball referees? movement during a basketball action. Presentation of time series stemming from the aforementioned problem, by using traditional Multilayered Perceptron neural networks (MLP), leads to a sort of paradox of backward time lapse effect that certain input and hidden layers nodes have on output nodes that correspond to previous moments in time. This paper describes conducted research and analysis of different methods of overcoming the presented problem. Presented paper is essentially split into two parts. First part gives insight on efforts that are put into training set configuration on standard Multi Layered Perceptron back propagation neural networks, in order to decrease backwards time lapse effects that certain input and hidden layers nodes have on output nodes. Second part of paper focuses on the results that a new neural network structure called LTR - MDTS provides. Foundation of LTR - MDTS design relies on a foundation on standard MLP neural networks with certain, left-to-right synapse removal to eliminate aforementioned backwards time lapse effect on the output nodes.

scholarly journals A Method of Speech Coding for Speech Recognition Using a Convolutional Neural Network

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1185 ◽  
Author(s):  
Mariusz Kubanek ◽  
Janusz Bobulski ◽  
Joanna Kulawik
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Recognition ◽  
Network Structure ◽  
High Resistance ◽  
Vocal Tract ◽  
Continuous Speech ◽  
Mel Frequency Cepstral Coefficients ◽  
New Approach ◽  
Neural Network Structure

This work presents a new approach to speech recognition, based on the specific coding of time and frequency characteristics of speech. The research proposed the use of convolutional neural networks because, as we know, they show high resistance to cross-spectral distortions and differences in the length of the vocal tract. Until now, two layers of time convolution and frequency convolution were used. A novel idea is to weave three separate convolution layers: traditional time convolution and the introduction of two different frequency convolutions (mel-frequency cepstral coefficients (MFCC) convolution and spectrum convolution). This application takes into account more details contained in the tested signal. Our idea assumes creating patterns for sounds in the form of RGB (Red, Green, Blue) images. The work carried out research for isolated words and continuous speech, for neural network structure. A method for dividing continuous speech into syllables has been proposed. This method can be used for symmetrical stereo sound.

scholarly journals Feedforward Neural Networks with a Hidden Layer Regularization Method

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 525 ◽  
Author(s):  
Habtamu Alemu ◽  
Wei Wu ◽  
Junhong Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Structure ◽  
Regularization Method ◽  
Error Function ◽  
Feedforward Neural Networks ◽  
Training Process ◽  
Regularization Term ◽  
Neural Network Structure ◽  
Hidden Layer

In this paper, we propose a group Lasso regularization term as a hidden layer regularization method for feedforward neural networks. Adding a group Lasso regularization term into the standard error function as a hidden layer regularization term is a fruitful approach to eliminate the redundant or unnecessary hidden layer neurons from the feedforward neural network structure. As a comparison, a popular Lasso regularization method is introduced into standard error function of the network. Our novel hidden layer regularization method can force a group of outgoing weights to become smaller during the training process and can eventually be removed after the training process. This means it can simplify the neural network structure and it minimizes the computational cost. Numerical simulations are provided by using K-fold cross-validation method with K = 5 to avoid overtraining and to select the best learning parameters. The numerical results show that our proposed hidden layer regularization method prunes more redundant hidden layer neurons consistently for each benchmark dataset without loss of accuracy. In contrast, the existing Lasso regularization method prunes only the redundant weights of the network, but it cannot prune any redundant hidden layer neurons.

scholarly journals Multi-scale target detection based on morphological shared-weight neural network

2017 ◽  
Author(s):  
◽  
Shuxian Shen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Convolutional Neural Networks ◽  
Network Structure ◽  
Training Data ◽  
Multi Scale ◽  
Neural Network Structure

Convolutional Neural Networks (CNN) are a popular neural network structure for image based applications. This thesis discusses an alternative network, the morphological shared-weight neural network (MSNN) for object detection. In this thesis, three combined network structures are developed for multi-scale object detection. The dataset used for the experiments presented here were created by the author for this thesis study. The convolutional neural network is used as the baseline for judging the performance of the MSNN. Experiments suggest that when training data is limited, the MSNN has a more robust and precise performance as compared with the CNN.

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